Acoustic source separation based on target equalization-cancellation

Normal-hearing listeners are good at focusing on the target talker while ignoring the interferers in a multi-talker environment. Therefore, efforts have been devoted to build psychoacoustic models to understand binaural processing in multi-talker environments and to develop bio-inspired source separation algorithms for hearing-assistive devices. This thesis presents a target-Equalization-Cancellation (target-EC) approach to the source separation problem. The idea of the target-EC approach is to use the energy change before and after cancelling the target to estimate a time-frequency (T-F) mask in which each entry estimates the strength of target signal in the original mixture. Once the mask is calculated, it is applied to the original mixture to preserve the target-dominant T-F units and to suppress the interferer-dominant T-F units. On the psychoacoustic modeling side, when the output of the target-EC approach is evaluated with the Coherence-based Speech Intelligibility Index (CSII), the predicted binaural advantage closely matches the pattern of the measured data. On the application side, the performance of the target-EC source separation algorithm was evaluated by psychoacoustic measurements using both a closed-set speech corpus and an open-set speech corpus, and it was shown that the target-EC cue is a better cue for source separation than the interaural difference cues

Single-Microphone Speech Enhancement and Separation Using Deep Learning

The cocktail party problem comprises the challenging task of understanding a speech signal in a complex acoustic environment, where multiple speakers and background noise signals simultaneously interfere with the speech signal of interest. A signal processing algorithm that can effectively increase the speech intelligibility and quality of speech signals in such complicated acoustic situations is highly desirable. Especially for applications involving mobile communication devices and hearing assistive devices. Due to the re-emergence of machine learning techniques, today, known as deep learning, the challenges involved with such algorithms might be overcome. In this PhD thesis, we study and develop deep learning-based techniques for two sub-disciplines of the cocktail party problem: single-microphone speech enhancement and single-microphone multi-talker speech separation. Specifically, we conduct in-depth empirical analysis of the generalizability capability of modern deep learning-based single-microphone speech enhancement algorithms. We show that performance of such algorithms is closely linked to the training data, and good generalizability can be achieved with carefully designed training data. Furthermore, we propose uPIT, a deep learning-based algorithm for single-microphone speech separation and we report state-of-the-art results on a speaker-independent multi-talker speech separation task. Additionally, we show that uPIT works well for joint speech separation and enhancement without explicit prior knowledge about the noise type or number of speakers. Finally, we show that deep learning-based speech enhancement algorithms designed to minimize the classical short-time spectral amplitude mean squared error leads to enhanced speech signals which are essentially optimal in terms of STOI, a state-of-the-art speech intelligibility estimator.Comment: PhD Thesis. 233 page

Sustainable Pavement Engineering and Road Materials

In a similar way to many other engineering fields, the road pavement industry strongly affects the critical issues of our generation, including climate change, pollutant emission, the exploitation of natural resources and economic crises. For this reason, technicians and researchers are searching ravenously for sustainable solutions to implement in current road construction systems with the following goals: To reduce the consumption of energy and virgin materials; To run environmentally and economically friendly maintenance; To recycle waste from different industrial processes; To decrease the noise, the pollution and the heat generated by traffic, particularly in urban contexts. This Special Issue aims to collect high-quality studies that combine the aforementioned solutions, including works pertaining to: The hot, warm, and cold recycling of reclaimed asphalt pavement; Marginal materials for asphalt pavements; Innovative sustainable materials; Durability and environmental aspects; Structure performance, modeling and design; Advanced trends in rehabilitation and preservation; Surface characteristics and road safety; Management system/life cycle analysis; Urban heat island mitigation; Energy harvesting

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 138

This special bibliography lists 343 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1975